CN111137282A - Vehicle collision prediction method and device, vehicle and electronic equipment - Google Patents
Vehicle collision prediction method and device, vehicle and electronic equipment Download PDFInfo
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Abstract
The invention discloses a collision prediction method and device for a vehicle, the vehicle and an electronic device. The method comprises the following steps: acquiring driving data of each vehicle driving on a road; identifying a second vehicle within a preset range of a first vehicle by taking one of the vehicles as the first vehicle according to the running data of the first vehicle; predicting a travel track of the first vehicle and a travel track of the second vehicle based on travel data of the first vehicle and travel data of the second vehicle, respectively; identifying whether there is a risk of collision between the first vehicle and the second vehicle based on the two travel trajectories. The method utilizes the cloud to store and analyze the driving data of the vehicles, reduces the data storage space of the vehicle side, saves the cost, is convenient for later-stage data maintenance and updating, and further can effectively identify whether collision risks exist among the vehicles, improve the safety performance of the vehicles and ensure the personal safety of users.
Description
Technical Field
The present invention relates to the field of vehicle collision prediction technologies, and in particular, to a method and an apparatus for predicting a vehicle collision, a vehicle, an electronic device, and a computer-readable storage medium.
Background
During the driving process of the vehicle, if a driver cannot see the front or the rear vehicle clearly due to bad weather, such as heavy fog, heavy rain, or obstruction, the distance between the vehicles is likely to be determined badly, and unreasonable driving behaviors are performed, for example, when the distance between the driver and the front vehicle is too small, a lane change is selected, so that a collision accident is likely to occur, and even the personal safety of the user is harmed.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above. Therefore, an object of the present invention is to provide a vehicle collision prediction method, which utilizes a cloud to store and analyze vehicle driving data, reduces a data storage space on a vehicle side, saves cost, facilitates later data maintenance and update, has high flexibility and reliability, further can effectively identify whether a collision risk exists between vehicles, improves the safety performance of vehicles, and ensures the personal safety of users.
A second object of the present invention is to provide a collision prediction apparatus for a vehicle.
A third object of the invention is to propose a vehicle.
A fourth object of the invention is to propose an electronic device.
A fifth object of the present invention is to propose a computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a collision prediction method for a vehicle, including: acquiring driving data of each vehicle driving on a road; identifying a second vehicle within a preset range of a first vehicle by taking one of the vehicles as the first vehicle according to the running data of the first vehicle; predicting a travel track of the first vehicle and a travel track of the second vehicle based on travel data of the first vehicle and travel data of the second vehicle, respectively; identifying whether there is a risk of collision between the first vehicle and the second vehicle based on the two travel trajectories.
According to the collision prediction method of the vehicle, the driving data of each vehicle driving on the road is acquired, then one vehicle is used as a first vehicle, a second vehicle within a preset range of the first vehicle is identified according to the driving data of the first vehicle, then the driving track of the first vehicle and the driving track of the second vehicle are predicted according to the driving data of the first vehicle and the driving data of the second vehicle respectively, and finally whether the first vehicle and the second vehicle have collision risks or not is identified according to the two driving tracks. The method utilizes the cloud to store and analyze the driving data of the vehicles, reduces the data storage space of the vehicle side, saves the cost, is convenient for later-stage data maintenance and updating, has higher flexibility and reliability, further can effectively identify whether collision risks exist among the vehicles, improves the safety performance of the vehicles, and ensures the personal safety of users.
In addition, the collision prediction method for a vehicle according to the above embodiment of the present invention may further have the following additional technical features:
in one embodiment of the present invention, the identifying whether the first vehicle and the second vehicle have a collision risk based on two of the travel tracks includes: and comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the running track of the first vehicle and the running track of the second vehicle have overlapped position points in the same time period, and identifying that the first vehicle and the second vehicle have collision risks.
In one embodiment of the present invention, the identifying whether the first vehicle and the second vehicle have a collision risk based on two of the travel tracks includes: comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the first vehicle is positioned in front of the second vehicle, and identifying that the first vehicle and the second vehicle have a collision risk if the running speed of the second vehicle is greater than that of the first vehicle.
In one embodiment of the present invention, the identifying whether the first vehicle and the second vehicle have a collision risk based on two of the travel tracks includes: comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the first vehicle is positioned behind the second vehicle, and identifying that the first vehicle and the second vehicle have a collision risk if the running speed of the first vehicle is greater than that of the second vehicle.
In one embodiment of the present invention, the identifying whether the first vehicle and the second vehicle have a collision risk based on two of the travel tracks includes: comparing the running track of the first vehicle with the running track of the second vehicle, identifying whether a vehicle needing lane changing exists in the first vehicle and the second vehicle, and if the vehicle needing lane changing exists, acquiring the inter-vehicle distance between the first vehicle and the second vehicle; and recognizing that the vehicle-to-vehicle distance is smaller than a safe vehicle-to-vehicle distance, and recognizing that the first vehicle and the second vehicle have a collision risk.
In one embodiment of the present invention, the collision prediction method of a vehicle further includes: obtaining a type of collision risk of the first vehicle and the second vehicle; issuing matched early warning information to the first vehicle and/or the second vehicle according to the type of the collision risk; or sending an emergency control instruction matched with the collision type to the first vehicle and/or the second vehicle according to the type of the collision risk.
In order to achieve the above object, a second aspect of the present invention provides a collision prediction apparatus for a vehicle, including: the data acquisition module is used for acquiring the driving data of each vehicle which is driving on a road; the vehicle identification device comprises an identification module, a judgment module and a control module, wherein the identification module is used for identifying a second vehicle in a preset range of a first vehicle by taking one vehicle as the first vehicle according to the running data of the first vehicle; a trajectory prediction module configured to predict a travel trajectory of the first vehicle and a travel trajectory of the second vehicle based on travel data of the first vehicle and travel data of the second vehicle, respectively; and the risk identification module is used for identifying whether the first vehicle and the second vehicle have collision risks or not based on the two running tracks.
According to the collision prediction device of the vehicle, the data acquisition module is used for acquiring the running data of each vehicle running on a road, the recognition module is used for recognizing the second vehicle within the preset range of the first vehicle by taking one vehicle as the first vehicle according to the running data of the first vehicle, the track prediction module is used for predicting the running track of the first vehicle and the running track of the second vehicle according to the running data of the first vehicle and the running data of the second vehicle, and the risk recognition module is used for recognizing whether the first vehicle and the second vehicle have collision risks or not according to the two running tracks. The device utilizes the high in the clouds storage, the data storage space of analysis vehicle, has reduced the data storage space of vehicle side, saves the cost, and the later stage data maintenance and the update of being convenient for have higher flexibility and reliability, and further, can effectively discern whether there is the collision risk between the vehicle, improve the security performance of vehicle, ensured user's personal safety.
In addition, the collision prediction apparatus for a vehicle according to the above embodiment of the present invention may further have the following additional technical features:
in an embodiment of the present invention, the risk identification module is specifically configured to: and comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the running track of the first vehicle and the running track of the second vehicle have overlapped position points in the same time period, and identifying that the first vehicle and the second vehicle have collision risks.
In an embodiment of the present invention, the risk identification module is further configured to: comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the first vehicle is positioned in front of the second vehicle, and identifying that the first vehicle and the second vehicle have a collision risk if the running speed of the second vehicle is greater than that of the first vehicle.
In an embodiment of the present invention, the risk identification module is further configured to: comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the first vehicle is positioned behind the second vehicle, and identifying that the first vehicle and the second vehicle have a collision risk if the running speed of the first vehicle is greater than that of the second vehicle.
In an embodiment of the present invention, the risk identification module is further configured to: comparing the running track of the first vehicle with the running track of the second vehicle, identifying whether a vehicle needing lane changing exists in the first vehicle and the second vehicle, and if the vehicle needing lane changing exists, acquiring the inter-vehicle distance between the first vehicle and the second vehicle; and recognizing that the vehicle-to-vehicle distance is smaller than a safe vehicle-to-vehicle distance, and recognizing that the first vehicle and the second vehicle have a collision risk.
In one embodiment of the present invention, the collision prediction apparatus for a vehicle further includes: a sending module, configured to obtain a type of collision risk between the first vehicle and the second vehicle; issuing matched early warning information to the first vehicle and/or the second vehicle according to the type of the collision risk; or sending an emergency control instruction matched with the collision type to the first vehicle and/or the second vehicle according to the type of the collision risk.
In order to achieve the above object, a third aspect of the present invention provides a vehicle including the collision prediction apparatus of the vehicle according to the second aspect of the present invention.
According to the vehicle provided by the embodiment of the invention, the driving data of the vehicle is stored and analyzed by using the cloud, the data storage space at the vehicle side is reduced, the cost is saved, the later-stage data maintenance and updating are facilitated, the flexibility and the reliability are higher, further, whether collision risks exist among the vehicles can be effectively identified, the safety performance of the vehicle is improved, and the personal safety of a user is guaranteed.
In order to achieve the above object, a fourth aspect of the present invention provides an electronic device, including a memory, a processor; wherein the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the collision prediction method for a vehicle according to the embodiment of the first aspect of the present invention.
According to the electronic equipment provided by the embodiment of the invention, the processor executes the computer program stored on the memory, and the driving data of the vehicle is stored and analyzed by using the cloud end, so that the data storage space of the vehicle side is reduced, the cost is saved, the later-stage data maintenance and updating are facilitated, the flexibility and the reliability are higher, further, whether collision risks exist among the vehicles can be effectively identified, the safety performance of the vehicles is improved, and the personal safety of users is guaranteed.
To achieve the above object, a fifth aspect of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the collision prediction method for a vehicle according to the first aspect of the present invention.
The computer-readable storage medium stores the computer program and is executed by the processor, and the driving data of the vehicle is stored and analyzed by using the cloud, so that the data storage space of the vehicle side is reduced, the cost is saved, the later-stage data maintenance and updating are facilitated, the flexibility and the reliability are higher, further, whether collision risks exist among the vehicles can be effectively identified, the safety performance of the vehicles is improved, and the personal safety of users is guaranteed.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a collision prediction method of a vehicle according to one embodiment of the invention;
FIG. 2 is a schematic diagram of a collision prediction method of a vehicle according to one embodiment of the invention;
fig. 3 is a schematic diagram of a collision prediction method of a vehicle according to another embodiment of the present invention;
fig. 4 is a schematic diagram of a collision prediction method of a vehicle according to another embodiment of the present invention;
FIG. 5 is a block schematic diagram of a collision prediction apparatus for a vehicle according to one embodiment of the present invention;
fig. 6 is a block schematic diagram of a collision prediction apparatus of a vehicle according to another embodiment of the present invention;
FIG. 7 is a block schematic diagram of a vehicle according to one embodiment of the present invention; and
FIG. 8 is a block diagram of an electronic device in accordance with one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A collision prediction method of a vehicle, an apparatus, a vehicle, an electronic device, and a computer-readable storage medium according to embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a collision prediction method of a vehicle according to one embodiment of the present invention. In an embodiment of the present invention, the vehicle may include an electric car, a fuel car, and the like.
As shown in fig. 1, a collision prediction method for a vehicle according to an embodiment of the present invention includes the steps of:
s101, driving data of each vehicle driving on a road is acquired.
It should be noted that, in the embodiment of the present invention, the cloud is used as an execution subject. The cloud end can establish network connection with the vehicle to carry out data transmission with the vehicle, and after the vehicle acquires the driving data of the vehicle, the driving data can be sent to the cloud end through network connection. Alternatively, the network connection may be a mobile network, such as 3G, 4G, 5G, etc.
Therefore, the method can send the driving information to the cloud after the vehicle acquires the driving information of the vehicle, so that the cloud acquires the driving data of each vehicle driving on the road, the data storage space of the vehicle side is reduced, the cost is saved, furthermore, the driving data of each vehicle is stored and analyzed through the cloud, the later-stage data maintenance and updating are facilitated, and the method has high flexibility and reliability.
The travel data may include information such as a position of the vehicle, a vehicle speed, an acceleration, a steering, a size of the vehicle, and the like.
Alternatively, the vehicle may be provided with a Positioning device to detect position information of the vehicle, and the Positioning device may be implemented based on a Positioning System such as a Global Positioning System (GPS), a bei dou navigation Satellite System (BDS), and may be a GPS receiver, a BDS receiver, or the like.
Alternatively, the vehicle speed and acceleration information may be detected by mounting a speed sensor and an acceleration sensor on the vehicle, respectively, the steering information of the vehicle may be acquired by mounting a position sensor on the steering wheel of the vehicle to detect the rotation angle and the rotation direction of the steering wheel, and the size information of the vehicle may be preset in a storage space of the vehicle according to the size information of the vehicle described in the vehicle instruction manual, for example, may be stored in the vehicle controller.
Alternatively, in order to distinguish the acquired travel information of the vehicles, identification information of each vehicle traveling on the road may also be acquired. The identification information may be a license plate number of the vehicle or a unique identification code configured when the vehicle leaves a factory, and the identification information may be preset in a storage space of the vehicle, for example, may be stored in a vehicle controller.
And S102, identifying a second vehicle in a preset range of the first vehicle by taking one of the vehicles as the first vehicle according to the running data of the first vehicle.
In an embodiment of the invention, the cloud may arbitrarily select one vehicle from the acquired driving data of the vehicles to serve as the first vehicle, and then identify the vehicle within the preset range of the first vehicle as the second vehicle according to the position information of the first vehicle and the acquired position information of the other vehicles. It should be noted that each vehicle traveling on the road may be regarded as a first vehicle, and the second vehicle within the preset range thereof may be identified based on the traveling data of each vehicle, respectively. It should be noted that the preset range is a critical range for determining whether there is a collision risk between vehicles. When the vehicle is in the preset range of another vehicle, the distance between the vehicles is smaller, the collision risk is larger, and in order to avoid vehicle collision, the collision risk between the vehicles can be identified; when the vehicle is not in the preset range of another vehicle, the distance between the vehicles is larger, the collision risk is smaller, and the fact that the collision risk does not exist between the vehicles can be identified.
Therefore, the method can identify the vehicles within the preset range of the vehicles according to the size relationship between the distance between the vehicles and the preset range of the vehicles, and then execute the subsequent steps, so that the efficiency is high.
Optionally, the preset range may be calibrated according to actual conditions, and may be preset in the storage space of the cloud. For example, the preset range may be calibrated according to the size of the vehicle, and optionally, the preset range may be updated according to the vehicle speed, weather, and other factors, for example, when the vehicle speed is fast, or when the weather is bad, such as heavy fog, heavy rain, and the like, the vehicles are more likely to collide with each other, and in order to avoid vehicle collision, the preset range may be appropriately increased, and the flexibility is higher.
For example, for a vehicle of a common size, the predetermined range may be designated 100m when the vehicle speed is 100 km/h. That is, the vehicle may be regarded as a first vehicle, and a vehicle having a distance of 100m or less from the vehicle may be identified as a second vehicle.
Further, after the second vehicle in the preset range of the first vehicle is identified, the position information of the first vehicle and the position information of the second vehicle can be obtained in real time, the position information of the first vehicle and the position information of the second vehicle are updated, whether the second vehicle is still in the preset range of the first vehicle is identified by using the updated position information of the first vehicle and the updated position information of the second vehicle, and if the second vehicle is not in the preset range of the first vehicle, the second vehicle does not belong to the second vehicle. The method can update the second vehicle in real time, and guarantees the accuracy and efficiency of prediction.
And S103, predicting the running track of the first vehicle and the running track of the second vehicle according to the running data of the first vehicle and the running data of the second vehicle respectively.
In one embodiment of the present invention, the travel track of the vehicle may be predicted based on the position information and the steering information of the vehicle.
For example, as shown in fig. 2, if the previous position of the vehicle is obtained as point a, the current position of the vehicle is obtained as point B, and it is detected that the current steering wheel of the vehicle is rotated to the left and the rotation angle is greater than the preset angle, the driving track of the vehicle can be predicted to be a B-C-D curve. The preset angle can be calibrated according to actual conditions, for example, the preset angle can be calibrated to 10 degrees and can be preset in the storage space of the cloud.
As another possible manner, the travel locus of the vehicle may be predicted based on the end point information of the vehicle. Wherein the end point information may be acquired according to a navigation system of the vehicle.
It should be noted that the driving track includes position points of the vehicle at different time points.
And S104, identifying whether the first vehicle and the second vehicle have collision risks or not based on the two running tracks.
In one embodiment of the invention, identifying whether the first vehicle and the second vehicle have the risk of collision based on the two travel tracks may include comparing the travel track of the first vehicle with the travel track of the second vehicle, and identifying whether the first vehicle and the second vehicle have the risk of collision according to the comparison result.
The comparison of the driving track of the first vehicle and the driving track of the second vehicle may include obtaining position points and corresponding time points on the driving tracks of the first vehicle and the second vehicle, respectively, and then comparing the position point of the first vehicle and the corresponding time point with the position point of the second vehicle and the corresponding time point.
Optionally, if it is identified that the travel track of the first vehicle and the travel track of the second vehicle have overlapping position points within the same time period, it is identified that the first vehicle and the second vehicle have a collision risk.
Therefore, when the predicted driving tracks of the two vehicles have coincident position points in the same time period, the method can effectively identify the collision risk between the vehicles, and improves the safety performance of the vehicles.
For example, as shown in fig. 3, if the vehicle a is a first vehicle, the vehicle B is a second vehicle, the travel track of the vehicle a is a track a ', the travel track of the vehicle B is a track B', the track a 'and the track B' are known to coincide at point O, and if the vehicle a and the vehicle B are predicted to pass through the point O in the same time period, it is possible to identify that there is a collision risk between the vehicle a and the vehicle B in order to avoid collision between the vehicle a and the vehicle B.
Optionally, identifying whether the first vehicle and the second vehicle have a collision risk based on the two travel tracks may include comparing the travel track of the first vehicle with the travel track of the second vehicle, and if it is identified that the first vehicle is located in front of the second vehicle and the travel speed of the second vehicle is greater than that of the first vehicle, it is described that the distance between the second vehicle and the first vehicle tends to decrease, the collision risk increases, and in order to avoid collision of the vehicles, it is identified that the first vehicle and the second vehicle have a collision risk.
Optionally, identifying whether the first vehicle and the second vehicle have a collision risk based on the two travel tracks may include comparing the travel track of the first vehicle with the travel track of the second vehicle, identifying that the first vehicle is located behind the second vehicle, and the travel speed of the first vehicle is greater than that of the second vehicle, which indicates that the distance between the first vehicle and the second vehicle tends to decrease, the collision risk increases, and identifying that the first vehicle and the second vehicle have a collision risk in order to avoid collision of the vehicles.
Therefore, the method can effectively identify the front and rear collision risk between the vehicles according to the predicted running tracks and the running speeds of the two vehicles, and improves the safety performance of the vehicles.
Optionally, identifying whether the first vehicle and the second vehicle have a collision risk based on the two travel tracks may include comparing the travel track of the first vehicle with the travel track of the second vehicle, identifying whether the first vehicle and the second vehicle have a vehicle that needs lane change, if there is a vehicle that needs lane change, when the vehicle changes lane, the possibility that the travel track of the vehicle overlaps with the travel tracks of other vehicles may increase, that is, the possibility of collision between the vehicles may increase, to avoid collision of the vehicles, the inter-vehicle distance between the first vehicle and the second vehicle may be obtained, if the inter-vehicle distance is identified to be smaller than the safe inter-vehicle distance, the inter-vehicle distance is smaller, the possibility of collision between the vehicles is higher, and to avoid collision between the vehicles, the first vehicle and the second vehicle may be identified to have a collision risk.
Therefore, the method can effectively identify the collision risk possibly caused by lane change between the vehicles according to the predicted running tracks and the inter-vehicle distance of the two vehicles, and improves the safety performance of the vehicles.
It should be noted that the safe inter-vehicle distance can be calibrated according to actual conditions, and can be preset in the storage space of the cloud. For example, the safe inter-vehicle distance may be calibrated according to the size of the vehicle, optionally, the safe inter-vehicle distance may be updated according to the vehicle speed, weather, and other factors, for example, when the vehicle speed is fast, or when the weather is bad, such as under heavy fog, heavy rain, and the like, the inter-vehicle collision is easier, and in order to avoid the vehicle collision, the safe inter-vehicle distance may be appropriately increased, and the flexibility is higher.
Alternatively, whether the vehicle needs to change lanes or not may be identified according to an inclination angle of the running track of the vehicle with respect to the lane. Alternatively, when the lean angle is greater than the angle threshold, it may be identified that the vehicle needs to change lanes. The angle threshold value can be calibrated according to actual conditions, for example, the angle threshold value can be calibrated to 15 degrees, and the angle threshold value can be preset in a storage space of a cloud.
For example, as shown in fig. 4, if the vehicle a is a first vehicle, the vehicle B is a second vehicle, and the travel track of the vehicle a is track a ', the travel track of the vehicle B is track B ', it can be known that the current travel lane of the vehicle a is lane 1, the current travel lane of the vehicle B is lane 2, the inclination angle of the track a ' with respect to lane 1 is 30 °, and the track a ' is inclined toward lane 2, if the angle threshold is 15 °, it can be recognized that the inclination angle of the track a ' with respect to the lane is greater than the angle threshold, it can be recognized that the vehicle a needs to change lane from lane 1 to lane 2, the cloud can obtain the current distance AB of the vehicles a and B, and call out the safe inter-vehicle distance of the vehicles a and B from the own storage space, if the current distance AB is 50m, the safe inter-vehicle distance is 80m, it can be recognized that the inter-vehicle distance is, in order to avoid a collision of the vehicle a with the vehicle B, it can be identified that there is a risk of collision of the vehicle a with the vehicle B.
Further, after the first vehicle and the second vehicle are identified to have collision risks, the types of the collision risks of the first vehicle and the second vehicle can be obtained, and then matched early warning information is sent to the first vehicle and/or the second vehicle according to the types of the collision risks so as to remind a driver of the existence of the collision risks, or an emergency control instruction matched with the collision types is sent to the first vehicle and/or the second vehicle according to the types of the collision risks so as to control the vehicles to execute corresponding instructions and avoid the collision risks.
It should be noted that the collision risk and the type of the collision risk corresponding to the collision risk, or the type of the collision risk and the early warning information matched with the collision risk, or the type of the collision risk and the emergency control instruction matched with the collision risk may be calibrated according to actual conditions, and are preset in the storage space of the cloud.
Optionally, the collision risk between vehicles may be calibrated into several risk types in advance, different types correspond to different risk levels, and the lower the risk level is, the smaller the collision risk is, and the smaller the personal safety hazard to the user is. Therefore, the risk type with a low risk level has little harm to the personal safety of the user, and can only send matched early warning information to the vehicle to remind the driver of collision risk, so that the driver can make reasonable driving behaviors to avoid the collision risk; the risk type with high risk level has great harm to the personal safety of the user, and if the driver does not timely perform corresponding behaviors, serious consequences can be caused, so that the matched emergency control instruction can be directly sent to the vehicle at the moment to control the vehicle to execute the corresponding instruction, and the collision risk is avoided.
Therefore, the method can send the matched early warning information to the vehicle or send the matched emergency control instruction to the vehicle according to the risk type corresponding to the collision risk, so that the collision risk can be effectively avoided, and the safety performance of the vehicle is improved.
Optionally, the early warning information may be issued by a voice broadcast system of the vehicle, or the related early warning information may be displayed by the central control screen, for example, the collision animation may be displayed by the central control screen.
For example, if it is recognized that the first vehicle is located behind the second vehicle and the traveling speed of the first vehicle is greater than that of the second vehicle, it may be recognized that the first vehicle and the second vehicle have a risk of a front-rear collision, at this time, the inter-vehicle distance between the first vehicle and the second vehicle may be obtained, if the inter-vehicle distance is greater than a preset threshold, that is, the inter-vehicle distance is greater, the collision risk is smaller, it may be recognized that the risk type at this time is a low risk type, and the risk level is low, at this time, the warning information may be issued to the first vehicle, for example, the voice broadcasting system of the first vehicle may be controlled to broadcast a voice of "about to collide with the front vehicle, please decelerate" to remind the driver of the first vehicle to decelerate, so. It should be noted that the preset threshold may be calibrated according to actual conditions, and may be preset in the storage space of the cloud.
If the distance between vehicles is smaller than or equal to the preset threshold value, namely the distance between vehicles is smaller at the moment, the collision risk is larger, the risk type at the moment can be identified to be a high risk type, the risk level is high, and for the personal safety of a user, an emergency control instruction can be sent to the first vehicle, for example, a brake instruction can be sent to the first vehicle to control the first vehicle to brake, so that the first vehicle can decelerate in time to avoid the collision risk.
In summary, according to the collision prediction method for vehicles in the embodiment of the present invention, the driving data of each vehicle driving on the road is obtained, then one of the vehicles is used as the first vehicle, the second vehicle within the preset range of the first vehicle is identified according to the driving data of the first vehicle, then the driving track of the first vehicle and the driving track of the second vehicle are predicted according to the driving data of the first vehicle and the driving data of the second vehicle, and finally whether the first vehicle and the second vehicle have a collision risk is identified based on the two driving tracks. The method utilizes the cloud to store and analyze the driving data of the vehicles, reduces the data storage space of the vehicle side, saves the cost, is convenient for later-stage data maintenance and updating, has higher flexibility and reliability, further can effectively identify whether collision risks exist among the vehicles, improves the safety performance of the vehicles, and ensures the personal safety of users.
Fig. 5 is a block diagram schematically illustrating a collision prediction apparatus of a vehicle according to an embodiment of the present invention.
As shown in fig. 5, the collision prediction apparatus 200 of the vehicle according to the embodiment of the present invention includes a data acquisition module 11, an identification module 12, a trajectory prediction module 13, and a risk identification module 14.
The data acquisition module 11 is configured to acquire travel data of each vehicle traveling on a road.
The identification module 12 is configured to identify a second vehicle within a preset range of a first vehicle according to driving data of the first vehicle, where one of the vehicles is the first vehicle.
The trajectory prediction module 13 is configured to predict a travel trajectory of the first vehicle and a travel trajectory of the second vehicle according to the travel data of the first vehicle and the travel data of the second vehicle, respectively.
The risk identification module 14 is configured to identify whether a collision risk exists between the first vehicle and the second vehicle based on the two travel trajectories.
In an embodiment of the present invention, the risk identification module 14 is specifically configured to compare the traveling track of the first vehicle with the traveling track of the second vehicle, identify that there are overlapping position points in the same time period in the traveling track of the first vehicle and the traveling track of the second vehicle, and identify that there is a collision risk between the first vehicle and the second vehicle.
In an embodiment of the present invention, the risk identification module 14 is further configured to compare the traveling track of the first vehicle with the traveling track of the second vehicle, identify that the first vehicle is located in front of the second vehicle, and identify that the traveling speed of the second vehicle is greater than that of the first vehicle, and then identify that there is a collision risk between the first vehicle and the second vehicle.
In an embodiment of the present invention, the risk identification module 14 is further configured to compare the traveling track of the first vehicle with the traveling track of the second vehicle, identify that the first vehicle is located behind the second vehicle, and identify that the traveling speed of the first vehicle is greater than that of the second vehicle, and then identify that there is a collision risk between the first vehicle and the second vehicle.
In an embodiment of the present invention, the risk identification module 14 is further configured to compare the driving track of the first vehicle with the driving track of the second vehicle, identify whether there is a vehicle that needs lane change in the first vehicle and the second vehicle, and if there is a vehicle that needs lane change, obtain a vehicle-to-vehicle distance between the first vehicle and the second vehicle; and recognizing that the vehicle-to-vehicle distance is smaller than a safe vehicle-to-vehicle distance, and recognizing that the first vehicle and the second vehicle have a collision risk.
In an embodiment of the present invention, as shown in fig. 6, the collision prediction apparatus 100 of the vehicle further includes a sending module 15 for obtaining a type of collision risk between the first vehicle and the second vehicle; issuing matched early warning information to the first vehicle and/or the second vehicle according to the type of the collision risk; or sending an emergency control instruction matched with the collision type to the first vehicle and/or the second vehicle according to the type of the collision risk.
It should be noted that, for details that are not disclosed in the vehicle collision prediction apparatus according to the embodiment of the present invention, please refer to details that are disclosed in the vehicle collision prediction method according to the above embodiment of the present invention, and details are not repeated herein.
To sum up, the collision prediction apparatus for a vehicle according to an embodiment of the present invention first obtains the driving data of each vehicle driving on a road through a data obtaining module, then uses one of the vehicles as a first vehicle through an identification module, identifies a second vehicle within a preset range of the first vehicle according to the driving data of the first vehicle, then predicts the driving track of the first vehicle and the driving track of the second vehicle according to the driving data of the first vehicle and the driving data of the second vehicle through a track prediction module, and finally identifies whether there is a collision risk between the first vehicle and the second vehicle based on the two driving tracks through a risk identification module. The device utilizes the high in the clouds storage, the data storage space of analysis vehicle, has reduced the data storage space of vehicle side, saves the cost, and the later stage data maintenance and the update of being convenient for have higher flexibility and reliability, and further, can effectively discern whether there is the collision risk between the vehicle, improve the security performance of vehicle, ensured user's personal safety.
In order to implement the above embodiment, the present invention further provides a vehicle 200, as shown in fig. 7, including the collision prediction apparatus 100 of the vehicle.
According to the vehicle provided by the embodiment of the invention, the driving data of the vehicle is stored and analyzed by using the cloud, the data storage space at the vehicle side is reduced, the cost is saved, the later-stage data maintenance and updating are facilitated, the flexibility and the reliability are higher, further, whether collision risks exist among the vehicles can be effectively identified, the safety performance of the vehicle is improved, and the personal safety of a user is guaranteed.
In order to implement the above embodiments, the present invention further provides an electronic device 300, as shown in fig. 8, the electronic device 300 includes a memory 31 and a processor 32. Wherein the processor 32 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 31 for implementing the collision prediction method of the vehicle described above.
According to the electronic equipment provided by the embodiment of the invention, the processor executes the computer program stored on the memory, and the driving data of the vehicle is stored and analyzed by using the cloud end, so that the data storage space of the vehicle side is reduced, the cost is saved, the later-stage data maintenance and updating are facilitated, the flexibility and the reliability are higher, further, whether collision risks exist among the vehicles can be effectively identified, the safety performance of the vehicles is improved, and the personal safety of users is guaranteed.
In order to implement the above embodiments, the present invention also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the collision prediction method of the vehicle described above.
The computer-readable storage medium stores the computer program and is executed by the processor, and the driving data of the vehicle is stored and analyzed by using the cloud, so that the data storage space of the vehicle side is reduced, the cost is saved, the later-stage data maintenance and updating are facilitated, the flexibility and the reliability are higher, further, whether collision risks exist among the vehicles can be effectively identified, the safety performance of the vehicles is improved, and the personal safety of users is guaranteed.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A collision prediction method for a vehicle, characterized by comprising the steps of:
acquiring driving data of each vehicle driving on a road;
identifying a second vehicle within a preset range of a first vehicle by taking one of the vehicles as the first vehicle according to the running data of the first vehicle;
predicting a travel track of the first vehicle and a travel track of the second vehicle based on travel data of the first vehicle and travel data of the second vehicle, respectively;
identifying whether there is a risk of collision between the first vehicle and the second vehicle based on the two travel trajectories.
2. The method of claim 1, wherein said identifying whether the first vehicle and the second vehicle are at risk of collision based on the two travel trajectories comprises:
and comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the running track of the first vehicle and the running track of the second vehicle have overlapped position points in the same time period, and identifying that the first vehicle and the second vehicle have collision risks.
3. The method of claim 1, wherein said identifying whether the first vehicle and the second vehicle are at risk of collision based on the two travel trajectories comprises:
comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the first vehicle is positioned in front of the second vehicle, and identifying that the first vehicle and the second vehicle have a collision risk if the running speed of the second vehicle is greater than that of the first vehicle.
4. The method of claim 1, wherein said identifying whether the first vehicle and the second vehicle are at risk of collision based on the two travel trajectories comprises:
comparing the running track of the first vehicle with the running track of the second vehicle, identifying that the first vehicle is positioned behind the second vehicle, and identifying that the first vehicle and the second vehicle have a collision risk if the running speed of the first vehicle is greater than that of the second vehicle.
5. The method of claim 1, wherein said identifying whether the first vehicle and the second vehicle are at risk of collision based on the two travel trajectories comprises:
comparing the running track of the first vehicle with the running track of the second vehicle, identifying whether a vehicle needing lane changing exists in the first vehicle and the second vehicle, and if the vehicle needing lane changing exists, acquiring the inter-vehicle distance between the first vehicle and the second vehicle;
and recognizing that the vehicle-to-vehicle distance is smaller than a safe vehicle-to-vehicle distance, and recognizing that the first vehicle and the second vehicle have a collision risk.
6. The method of any one of claims 1-5, further comprising:
obtaining a type of collision risk of the first vehicle and the second vehicle;
issuing matched early warning information to the first vehicle and/or the second vehicle according to the type of the collision risk; alternatively, the first and second electrodes may be,
and according to the type of the collision risk, sending an emergency control instruction matched with the collision type to the first vehicle and/or the second vehicle.
7. A collision prediction apparatus for a vehicle, characterized by comprising:
the data acquisition module is used for acquiring the driving data of each vehicle which is driving on a road;
the vehicle identification device comprises an identification module, a judgment module and a control module, wherein the identification module is used for identifying a second vehicle in a preset range of a first vehicle by taking one vehicle as the first vehicle according to the running data of the first vehicle;
a trajectory prediction module configured to predict a travel trajectory of the first vehicle and a travel trajectory of the second vehicle based on travel data of the first vehicle and travel data of the second vehicle, respectively;
and the risk identification module is used for identifying whether the first vehicle and the second vehicle have collision risks or not based on the two running tracks.
8. A vehicle, characterized by comprising: the collision predicting device of a vehicle according to claim 7.
9. An electronic device comprising a memory, a processor;
wherein the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the collision prediction method of the vehicle according to any one of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a collision prediction method for a vehicle according to any one of claims 1 to 6.
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| CN201911225638.4A CN111137282A (en) | 2019-12-04 | 2019-12-04 | Vehicle collision prediction method and device, vehicle and electronic equipment |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111627252A (en) * | 2020-06-10 | 2020-09-04 | 上海商汤智能科技有限公司 | Vehicle early warning method and device, electronic equipment and storage medium |
| CN111731283A (en) * | 2020-05-26 | 2020-10-02 | 北京百度网讯科技有限公司 | Vehicle collision risk identification method and device and electronic equipment |
| CN111932882A (en) * | 2020-08-13 | 2020-11-13 | 广东飞达交通工程有限公司 | Real-time early warning system, method and equipment for road accidents based on image recognition |
| CN112141094A (en) * | 2020-09-28 | 2020-12-29 | 北京汽车研究总院有限公司 | Vehicle and anti-collision method and device thereof |
| CN113421460A (en) * | 2021-06-23 | 2021-09-21 | 中煤航测遥感集团有限公司 | Anti-collision early warning method and device for working vehicle, vehicle-mounted terminal and storage medium |
| WO2021237768A1 (en) * | 2020-05-29 | 2021-12-02 | 初速度（苏州）科技有限公司 | Data-driven-based system for implementing automatic iteration of prediction model |
| CN113879290A (en) * | 2020-07-02 | 2022-01-04 | 宝能汽车集团有限公司 | Vehicle and anti-collision control method and system thereof and storage medium |
| CN114076603A (en) * | 2020-08-18 | 2022-02-22 | 财团法人车辆研究测试中心 | Trajectory determination method |
| CN115731742A (en) * | 2021-08-26 | 2023-03-03 | 博泰车联网(南京)有限公司 | Collision prompt information output method and device, electronic equipment and readable storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103794086A (en) * | 2014-01-26 | 2014-05-14 | 浙江吉利控股集团有限公司 | Vehicle driving early warning method |
| CN104537889A (en) * | 2014-12-30 | 2015-04-22 | 四川九洲电器集团有限责任公司 | Anti-collision method and system under different vehicle conditions |
| CN107134173A (en) * | 2017-06-15 | 2017-09-05 | 长安大学 | A kind of lane-change early warning system and method recognized with driving habit |
| CN107170292A (en) * | 2017-06-30 | 2017-09-15 | 维沃移动通信有限公司 | A kind of driving safety prompt method and electronic equipment |
| CN108010388A (en) * | 2018-01-04 | 2018-05-08 | 北京瑞腾中天科技有限公司 | Collision detection method for early warning and collision detection early warning system based on car networking network |
| CN108791286A (en) * | 2018-06-21 | 2018-11-13 | 奇瑞汽车股份有限公司 | Driving collision avoidance method and apparatus |
-
2019
- 2019-12-04 CN CN201911225638.4A patent/CN111137282A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103794086A (en) * | 2014-01-26 | 2014-05-14 | 浙江吉利控股集团有限公司 | Vehicle driving early warning method |
| CN104537889A (en) * | 2014-12-30 | 2015-04-22 | 四川九洲电器集团有限责任公司 | Anti-collision method and system under different vehicle conditions |
| CN107134173A (en) * | 2017-06-15 | 2017-09-05 | 长安大学 | A kind of lane-change early warning system and method recognized with driving habit |
| CN107170292A (en) * | 2017-06-30 | 2017-09-15 | 维沃移动通信有限公司 | A kind of driving safety prompt method and electronic equipment |
| CN108010388A (en) * | 2018-01-04 | 2018-05-08 | 北京瑞腾中天科技有限公司 | Collision detection method for early warning and collision detection early warning system based on car networking network |
| CN108791286A (en) * | 2018-06-21 | 2018-11-13 | 奇瑞汽车股份有限公司 | Driving collision avoidance method and apparatus |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111731283A (en) * | 2020-05-26 | 2020-10-02 | 北京百度网讯科技有限公司 | Vehicle collision risk identification method and device and electronic equipment |
| WO2021237768A1 (en) * | 2020-05-29 | 2021-12-02 | 初速度（苏州）科技有限公司 | Data-driven-based system for implementing automatic iteration of prediction model |
| CN111627252A (en) * | 2020-06-10 | 2020-09-04 | 上海商汤智能科技有限公司 | Vehicle early warning method and device, electronic equipment and storage medium |
| CN113879290A (en) * | 2020-07-02 | 2022-01-04 | 宝能汽车集团有限公司 | Vehicle and anti-collision control method and system thereof and storage medium |
| CN111932882A (en) * | 2020-08-13 | 2020-11-13 | 广东飞达交通工程有限公司 | Real-time early warning system, method and equipment for road accidents based on image recognition |
| CN111932882B (en) * | 2020-08-13 | 2022-05-06 | 广东飞达交通工程有限公司 | Real-time early warning system, method and equipment for road accidents based on image recognition |
| CN114076603A (en) * | 2020-08-18 | 2022-02-22 | 财团法人车辆研究测试中心 | Trajectory determination method |
| CN114076603B (en) * | 2020-08-18 | 2024-04-16 | 财团法人车辆研究测试中心 | Track determination method |
| CN112141094A (en) * | 2020-09-28 | 2020-12-29 | 北京汽车研究总院有限公司 | Vehicle and anti-collision method and device thereof |
| CN113421460A (en) * | 2021-06-23 | 2021-09-21 | 中煤航测遥感集团有限公司 | Anti-collision early warning method and device for working vehicle, vehicle-mounted terminal and storage medium |
| CN113421460B (en) * | 2021-06-23 | 2023-01-24 | 中煤航测遥感集团有限公司 | Anti-collision early warning method and device for working vehicle, vehicle-mounted terminal and storage medium |
| CN115731742A (en) * | 2021-08-26 | 2023-03-03 | 博泰车联网(南京)有限公司 | Collision prompt information output method and device, electronic equipment and readable storage medium |
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